Inverse Probability Weighted Cox Regression for Doubly Truncated Data

Doubly truncated data arise when event times are observed only if they fall within subject-specific, possibly random, intervals. While non-parametric methods for survivor function estimation using doubly truncated data have been intensively studied, only a few methods for fitting regression models h...

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Bibliographic Details
Published in:Biometrics 2018-06, Vol.74 (2), p.481-487
Main Authors: Mandel, Micha, de Uña-Álvarez, Jacobo, Simon, David K., Betensky, Rebecca A.
Format: Article
Language:English
Subjects:
Age of Onset
Biased data
BIOMETRIC METHODOLOGY: DISCUSSION PAPER
biometry
Biometry - methods
computer software
Humans
Inverse weighting
Movement disorders
Neurodegenerative diseases
Parkinson disease
Parkinson Disease - genetics
Parkinson's disease
Polymorphism, Single Nucleotide
Probability
Proportional Hazards Models
Regression Analysis
Regression models
Right truncation
Single-nucleotide polymorphism
Software
Statistical analysis
U statistic
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Description
Summary:Doubly truncated data arise when event times are observed only if they fall within subject-specific, possibly random, intervals. While non-parametric methods for survivor function estimation using doubly truncated data have been intensively studied, only a few methods for fitting regression models have been suggested, and only for a limited number of covariates. In this article, we present a method to fit the Cox regression model to doubly truncated data with multiple discrete and continuous covariates, and describe how to implement it using existing software. The approach is used to study the association between candidate single nucleotide polymorphisms and age of onset of Parkinson's disease.
ISSN:0006-341X
1541-0420
DOI:10.1111/biom.12771